Zero Shot Information Extraction

Zero-shot information extraction (IE) aims to extract structured information from text without requiring any labeled training data, significantly reducing the time and effort needed for traditional IE systems. Current research heavily utilizes large language models (LLMs), particularly focusing on prompt engineering techniques like chain-of-thought prompting and question-answering paradigms to guide the LLMs' extraction capabilities. This approach shows promise for various IE tasks, including named entity recognition and relation extraction, offering a more efficient and scalable alternative to traditional methods, particularly beneficial for low-resource scenarios and domains with limited annotated data.